5. Proximity Analysis

Introduction

You are part of a crisis response team, and you want to identify how hospitals have been responding to crash collisions in New York City.

Before you get started, run the code cell below to set everything up.

import math
import pandas as pd
import geopandas as gpd
from geopy.geocoders import Nominatim            # What you'd normally run
#from learntools.geospatial.tools import Nominatim # Just for this exercise

import folium 
from folium import Marker
from folium.plugins import MarkerCluster

from folium.plugins import HeatMap
from folium import Marker, GeoJson

import pandas as pd
import geopandas as gpd

def embed_map(m, file_name):
    from IPython.display import IFrame
    m.save(file_name)
    return IFrame(file_name, width='100%', height='500px')

Exercises

1) Visualize the collision data.

Run the code cell below to load a GeoDataFrame collisions tracking major motor vehicle collisions in 2013-2018.

collisions = gpd.read_file("D:/archive (1)/NYPD_Motor_Vehicle_Collisions/NYPD_Motor_Vehicle_Collisions/NYPD_Motor_Vehicle_Collisions.shp")
collisions.head()

	DATE	TIME	BOROUGH	ZIP CODE	LATITUDE	LONGITUDE	LOCATION	ON STREET	CROSS STRE	OFF STREET	...	CONTRIBU_2	CONTRIBU_3	CONTRIBU_4	UNIQUE KEY	VEHICLE TY	VEHICLE _1	VEHICLE _2	VEHICLE _3	VEHICLE _4	geometry
0	07/30/2019	0:00	BRONX	10464	40.841100	-73.784960	(40.8411, -73.78496)	NaN	NaN	121 PILOT STREET	...	Unspecified	NaN	NaN	4180045	Sedan	Station Wagon/Sport Utility Vehicle	Station Wagon/Sport Utility Vehicle	NaN	NaN	POINT (1043750.211 245785.815)
1	07/30/2019	0:10	QUEENS	11423	40.710827	-73.770660	(40.710827, -73.77066)	JAMAICA AVENUE	188 STREET	NaN	...	NaN	NaN	NaN	4180007	Sedan	Sedan	NaN	NaN	NaN	POINT (1047831.185 198333.171)
2	07/30/2019	0:25	NaN	NaN	40.880318	-73.841286	(40.880318, -73.841286)	BOSTON ROAD	NaN	NaN	...	NaN	NaN	NaN	4179575	Sedan	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	POINT (1028139.293 260041.178)
3	07/30/2019	0:35	MANHATTAN	10036	40.756744	-73.984590	(40.756744, -73.98459)	NaN	NaN	155 WEST 44 STREET	...	NaN	NaN	NaN	4179544	Box Truck	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	POINT (988519.261 214979.320)
4	07/30/2019	10:00	BROOKLYN	11223	40.600090	-73.965910	(40.60009, -73.96591)	AVENUE T	OCEAN PARKWAY	NaN	...	NaN	NaN	NaN	4180660	Station Wagon/Sport Utility Vehicle	Bike	NaN	NaN	NaN	POINT (993716.669 157907.212)

5 rows × 30 columns

“LATITUDE” 및 “LONGITUDE” 열을 사용하여 충돌 데이터를 시각화하는 대화형 지도를 만듭니다. 어떤 유형의 지도가 가장 효과적이라고 생각하십니까?

m_1 = folium.Map(location=[40.7, -74], zoom_start=11) 

HeatMap(data=collisions[['LATITUDE', 'LONGITUDE']], radius=15).add_to(m_1)

m_1

Make this Notebook Trusted to load map: File -> Trust Notebook

2) Understand hospital coverage.

Run the next code cell to load the hospital data.

hospitals = gpd.read_file("D:/archive (1)/nyu_2451_34494/nyu_2451_34494/nyu_2451_34494.shp")
hospitals.head()

	id	name	address	zip	factype	facname	capacity	capname	bcode	xcoord	ycoord	latitude	longitude	geometry
0	317000001H1178	BRONX-LEBANON HOSPITAL CENTER - CONCOURSE DIVI...	1650 Grand Concourse	10457	3102	Hospital	415	Beds	36005	1008872.0	246596.0	40.843490	-73.911010	POINT (1008872.000 246596.000)
1	317000001H1164	BRONX-LEBANON HOSPITAL CENTER - FULTON DIVISION	1276 Fulton Ave	10456	3102	Hospital	164	Beds	36005	1011044.0	242204.0	40.831429	-73.903178	POINT (1011044.000 242204.000)
2	317000011H1175	CALVARY HOSPITAL INC	1740-70 Eastchester Rd	10461	3102	Hospital	225	Beds	36005	1027505.0	248287.0	40.848060	-73.843656	POINT (1027505.000 248287.000)
3	317000002H1165	JACOBI MEDICAL CENTER	1400 Pelham Pkwy	10461	3102	Hospital	457	Beds	36005	1027042.0	251065.0	40.855687	-73.845311	POINT (1027042.000 251065.000)
4	317000008H1172	LINCOLN MEDICAL & MENTAL HEALTH CENTER	234 E 149 St	10451	3102	Hospital	362	Beds	36005	1005154.0	236853.0	40.816758	-73.924478	POINT (1005154.000 236853.000)

“위도” 및 “경도” 열을 사용하여 병원 위치를 시각화합니다.

m_2 = folium.Map(location=[40.7, -74], zoom_start=11) 

for idx, row in hospitals.iterrows():
    Marker([row['latitude'], row['longitude']]).add_to(m_2)


m_2

Make this Notebook Trusted to load map: File -> Trust Notebook

3) 가장 가까운 병원이 10km 이상 떨어진 때는 언제였습니까?

가장 가까운 병원에서 10km 이상 떨어진 곳에서 충돌이 발생한 ’collisions’의 모든 행을 포함하는 DataFrame ’outside_range’를 만듭니다.

‘병원’과 ’충돌’ 모두 좌표 기준 시스템으로 EPSG 2263을 사용하고 EPSG 2263의 단위는 미터입니다.

coverage = gpd.GeoDataFrame(geometry=hospitals.geometry).buffer(10000)
my_union = coverage.geometry.unary_union
outside_range = collisions.loc[~collisions["geometry"].apply(lambda x: my_union.contains(x))]

다음 코드 셀은 가장 가까운 병원에서 10km 이상 떨어진 곳에서 발생한 충돌 비율을 계산합니다.

percentage = round(100*len(outside_range)/len(collisions), 2)
print("Percentage of collisions more than 10 km away from the closest hospital: {}%".format(percentage))

Percentage of collisions more than 10 km away from the closest hospital: 15.12%

4) Make a recommender.

먼 곳에서 충돌이 발생하면 부상자를 가장 가까운 병원으로 이송하는 것이 더욱 중요해집니다.

이를 염두에 두고 다음과 같은 추천자를 만들기로 결정합니다. - 충돌 위치(EPSG 2263에서)를 입력으로 사용합니다. - 가장 가까운 병원(EPSG 2263에서 거리 계산이 수행되는 곳)을 찾고, - 가장 가까운 병원의 이름을 반환합니다.

def best_hospital(collision_location):
    name = value_hospital
    return name

# Test your function: this should suggest CALVARY HOSPITAL INC
print(best_hospital(outside_range.geometry.iloc[0]))

NameError: name 'value_hospital' is not defined

5) 수요가 가장 많은 병원은?

‘outside_range’ DataFrame의 충돌만 고려했을 때 가장 추천하는 병원은?

응답은 4)에서 생성한 함수에서 반환한 병원 이름과 정확히 일치하는 Python 문자열이어야 합니다.

6) Where should the city construct new hospitals?

Run the next code cell (without changes) to visualize hospital locations, in addition to collisions that occurred more than 10 kilometers away from the closest hospital.

m_6 = folium.Map(location=[40.7, -74], zoom_start=11) 

coverage = gpd.GeoDataFrame(geometry=hospitals.geometry).buffer(10000)
folium.GeoJson(coverage.geometry.to_crs(epsg=4326)).add_to(m_6)
HeatMap(data=outside_range[['LATITUDE', 'LONGITUDE']], radius=9).add_to(m_6)
folium.LatLngPopup().add_to(m_6)

embed_map(m_6, 'm_6.html')